Department of Electrical and Electronics Engineering
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Item Development of a fuzzy-pi tuned bidirectional charger for electric three-wheeler applications(IEEE, 2025-05) Bansal, Hari Om; Singh, DheerendraThe growing worldwide market for electric vehicles (EVs) has put pressure on automotive system developers to improve charging efficiency and enhance the charging infrastructure. Bidirectional chargers support the grid by flowing the power in the two directions, i.e. functioning in both V2G and G2V modes. In this paper, a bidirectional converter interfaces a fixed DC bus that supports EV battery charging and discharging operation, is presented and simulated in a MATLAB environment. A Fuzzy Logic tuned Proportional Integral (FLPI) is designed using MATLAB Simulink to enhance the charger's performance. This paper also presents a performance comparative analysis of the FLPI and conventionally used PI Controller, highlighting the FLPI controller's advantages. FLPI is better than the conventional PI because of low errors, faster response time, better transient performance and improved robustness. The charger is developed and simulated for a 600V DC supply with a peak output power of 10kW. The simulation results demonstrate the charging and discharging of a 72V 150 Ah Lithium-ion (Li-ion) battery for electric-3-wheeler applications.Item Hardware-in-loop implementation of an adaptive MPPT controlled PV-assisted EV charging system with vehicle-to-grid integration(Springer Nature, 2025-08) Bansal, Hari OmThe penetration of electric vehicles (EVs) into society needs extensive charging infrastructure. The existing charging system solely depends on the grid supply, which is essentially fossil fuel-dependent and leads to carbon emissions and environmental pollution. This can be minimized by incorporating renewable energy into the charging grid. This article presents a charging scheme combining photovoltaic (PV) and grid, offering a clean and dependable charging plan to sustain green transport. The proposed work presents the modelling and controlling a 10 kW EV charging/discharging framework integrating PV and grid. This work has multi-fold objectives: i) the development of an intelligent hybrid maximum power point tracking (MPPT) strategy, ii) the design of a fuzzy logic controlled bidirectional charger, iii) the setup of a PV-grid integrated charging system, and iv) the implementation of vehicle-to-grid (V2G) operation. The proposed charging system utilizes PV power and seamlessly switches to grid power whenever required. Since the performance of the PV source is affected by varying temperatures and irradiance, MPPT methods are needed to extract maximum power from the PV source. This paper developed and compared perturb and observe (P&O), Particle swarm optimization (PSO), and hybrid PSO + Adaptive neuro-fuzzy inference system (ANFIS) based algorithm for MPPT. The findings indicate that the PSO + ANFIS-driven method offers the highest tracking efficiency of 99.5%. This algorithm is also tested under dynamic partial shading conditions (PSC) to ensure robustness, and it led to achieving fast convergence and high efficiency despite multiple power peaks. In addition, the designed bidirectional charging system maximizes solar energy collection, minimizes the charging cost, and improves grid stability through demand balancing. The overall system is validated in a hardware-in-loop real-time environment through FPGA-based OPAL-RT.Item Disturbance observer-based sliding mode control of boost-flyback converter(IEEE, 2025-04) Bansal, Hari Om; Gautam, Aditya R.This paper presents a disturbance observer based sliding mode control of single-input double-output Boost-flyback dc-dc converter. The converter supplies two outputs i.e. flyback side output and boost side output through common magnetic path of flyback transformer. The power flow through common magnetic path poses the challenges of cross-coupling and crossregulation at line and load transient operations of converter. In this paper, these challenges are alleviated using sliding mode control approach. Moreover, a disturbance observer is also designed to alleviate the effect of cross-regulation at the load transients. The performance of proposed control technique is validated using simulation results.Item Next Generation Systems and Networks(Springer, 2023) Bansal, Hari Om; Ajmera, Pawan K.; Joshi, SandeepThe book is a collection of high-quality research papers presented at International Conference on Next Generation Systems and Networks (BITS EEE CON 2022), held at Birla Institute of Technology & Science, Pilani, Rajasthan, India, during November 4–5, 2022. This book provides reliable and efficient design solutions for the next-generation networks and systems. The book covers research areas in energy, power and control; communication and signal processing; and electronics and nanotechnology.Item A Study on DC Fast Charging of Electric Vehicles(Springer, 2023-07) Bansal, Hari OmAs the penetration of electric vehicles (EVs) is increasing, their efficient charging becomes very important. This paper presents the simulation of various charging algorithms for the Li-Ion Batteries of EVs and their comparison with each other. Various charging algorithms like Constant Current (CC), Constant Voltage (CV), and Constant Current-Constant Voltage (CC/CV) algorithms have been discussed along with various DC-DC charging topologies like the Buck converters and the LLC Resonant Converter have been discussed. Finally, voltage matching algorithm has been proposed, simulated, and incorporated into the CC/CV algorithm and compared to previous results.Item An extensive review on hybrid electric vehicles powered by fuel cell-enabled hybrid energy storage system(Springer, 2023) Bansal, Hari OmTo overcome the air pollution and ill effects of IC engine-based transportation (ICEVs), demand of electric vehicles (EVs) has risen which reduce *gasoline consumption, environment degradation and energy wastage, but barriers—short driving range, higher battery cost and longer charging time—slow down its wide adoptions and commercialization. Although to overcome such issues, EV variants —HEVs and PHEVs—were also brought into the market but not that successful either. The use of ICE in HEVs and PHEVs increases fossil fuel dependency. Thus, the research focus shifted towards fuel cell-powered electric vehicles (FCEVs) which offer negligible emission and higher efficiency than EV variants. Though a moderate research work has been done on FCEVs, still its wide expansion is limited, facing severe challenges commonly related to fuel cost, selection of energy units, power electronic interfacing, component sizing and energy management. This paper presents an extensive exploration on EV variants, their issues, an in-depth comparison of latest topologies for FCEVs and optimum arrangement of HESS, designed by energy unit’s integration, i.e. FC, battery and UCs, to encounter the dynamic power demand and develop a performant model for transportation. In last, progress and possible future research areas are discussed. In short, this paper reveals all contemporary information of FCHEV technology to the scientists and scholars who are working in this particular arena.Item Experimental Investigations on Particle Swarm Optimization Based Control Algorithm for Shunt Active Power Filter to Enhance Electric Power Quality(IEEE, 2022-05) Bansal, Hari Om; Gautam, Aditya R.Quality power is a very important factor for the proper functioning of appliances and can be enhanced using shunt active power filters (SAPF). This work demonstrates the hardware implementation of synchronous reference frame (SRF) theory based SAPF. This paper presents the technique of particle swarm optimization (PSO) to tune the gain values of the SAPF PI controller to control the voltage of the DC-link and enhance its dynamic efficiency. The estimation of gain values of PI controllers using conventional methods does not yield the expected outcomes under varying load conditions. The PSO tuned controller provides better results as compared to traditional tuning methods. The switching scheme is implemented using hysteresis controller to control the SAPF. The main objectives of this approach are to extract the compensating currents and cancel out the harmonics produced by balanced, unbalanced and nonlinear loads. The planned scheme is designed and implemented in MATLAB/Simulink and then its performance on a developed laboratory prototype is validated experimentally.Item Transformer-based time series prediction of the maximum power point for solar photovoltaic cells(Wiley, 2022-06) Bansal, Hari Om; Gautam, Aditya R.This paper proposes an improved deep learning-based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series-based environmental inputs. Generally, artificial neural network-based MPPT algorithms use basic neural network architectures and inputs which do not represent the ambient conditions in a comprehensive manner. In this article, the ambient conditions of a location are represented through a comprehensive set of environmental features. Furthermore, the inclusion of time-based features in the input data is considered to model cyclic patterns temporally within the atmospheric conditions leading to robust modeling of the MPPT algorithm. A transformer-based deep learning architecture is trained as a time series prediction model using multidimensional time series input features. The model is trained on a dataset containing typical meteorological-year data points of ambient weather conditions from 50 locations. The attention mechanism in the transformer modules allows the model to learn temporal patterns in the data efficiently. The proposed model achieves a 0.47% mean average percentage error of prediction on non-zero operating voltage points in a test dataset consisting of data collected over a period of 200 consecutive hours; resulting in the average power efficiency of 99.54% and peak power efficiency of 99.98%. The proposed model is validated through real-time simulations. The proposed model performs power point tracking in a robust, dynamic, and nonlatent manner, over a wide range of atmospheric conditions.Item Adaptive artificial neural network based control strategy for shunt active power filter(IEEE, 2016) Ajmera, Pawan K.; Bansal, Hari OmShunt active power filter (SAPF) is used to mitigate the current harmonics and to improve the power factor. In this paper, Adaptive linear-neuron (ADALINE) based phase lock loop (PLL) controlling scheme is presented for SAPF. ADALINE networks estimate the fundamental supply frequency. This scheme detects the phase information of the supply voltage and also used for parallel computing as it provides faster convergence. This algorithm is trained by least-mean squares (LMS) rule which offers low computational burden on the system. In this work, ADALINE is tuned using particle swarm optimization (PSO) technique to improve the dynamic performance of the system. The results obtained are compared with conventional PLL control technique and are found to be significantly better. The performance of the proposed ADALINE based control algorithm is validated using MATLAB/Simulink.Item Feed forward modelling and real-time implementation of an intelligent fuzzy logic-based energy management strategy in a series-parallel hybrid electric vehicle to improve fuel economy(Springer, 2020-01) Singh, Dheerendra; Bansal, Hari OmA hybrid electric vehicle is powered by: the internal combustion engine and the battery-powered electric motor. These sources have specific operational characteristics, and it is necessary to match these characteristics for the efficient and smooth functioning of the vehicle. The nonlinearity and uncertainties in hybrid electric vehicle model require an intelligent controller to control the energy sharing between battery and engine. In this work, a fuzzy logic-enabled energy management strategy for the hybrid electric vehicle based on torque demand, battery state of charge and regenerative braking is designed and implemented. The proposed energy management strategy allows engine and motor to maneuver in their efficient operating regions. The designed hybrid electric vehicle and its control strategy follow the driver commands and regulations on vehicle performance and liquid fuel consumption. MATLAB/Simulink is used to carry out simulations, and then, the whole system is validated in real time on hardware-in-the-loop testing platform. This work employs an FPGA-based MicroLabBox hardware controller to validate real-time behavior. The proposed scheme results in better fuel economy, faster response and almost nil mismatch between desired and achieved vehicle speeds.